协同嵌套采样：大数据与复杂物理模型
Collaborative Nested Sampling: Big Data vs. complex physical models
AI总结：
本文提出协同嵌套采样算法，使物理模型评估次数随数据集数量次线性增长，从而在大数据场景下高效地为每个观测推导参数概率分布，无需对误差或模型形式作严格假设，可加速大型巡天和积分场单元观测分析。
摘要：
当前及未来天文巡天所释放的数据洪流要求可扩展的分析方法。许多机器学习方法具有良好的可扩展性，但将仪器测量与所关注的物理效应分离、处理可变误差以及推导参数不确定性往往只是事后才被考虑。基于马尔可夫链蒙特卡洛或嵌套采样的经典前向折叠分析即使在面对复杂且评估缓慢的物理模型时，也能实现参数估计和模型比较。然而，这些方法需要对每个数据集进行独立运行，这意味着在大数据场景下模型评估次数将达到不可行的程度。本文提出了一种新算法——协同嵌套采样，用于为每个观测推导参数概率分布。重要的是，物理模型评估次数随数据集数量呈次线性增长，并且无需对误差的同质性、高斯性、模型形式或观测的异质性/完整性作任何假设。协同嵌套采样可立即应用于加速大型巡天、积分场单元观测以及蒙特卡洛模拟的分析。
英文摘要：
The data torrent unleashed by current and upcoming astronomical surveys demands scalable analysis methods. Many machine learning approaches scale well, but separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is often an after-thought. Classic forward-folding analyses with Markov Chain Monte Carlo or Nested Sampling enable parameter estimation and model comparison, even for complex and slow-to-evaluate physical models. However, these approaches require independent runs for each data set, implying an unfeasible number of model evaluations in the Big Data regime. Here I present a new algorithm, collaborative nested sampling, for deriving parameter probability distributions for each observation. Importantly, the number of physical model evaluations scales sub-linearly with the number of data sets, and no assumptions about homogeneous errors, Gaussianity, the form of the model or heterogeneity/completeness of the observations need to be made. Collaborative nested sampling has immediate application in speeding up analyses of large surveys, integral-field-unit observations, and Monte Carlo simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
